AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks
Kibeom Hong, Seogkyu Jeon, Junsoo Lee, Namhyuk Ahn, Kunhee Kim,, Pilhyeon Lee, Daesik Kim, Youngjung Uh, Hyeran Byun

TL;DR
AesPA-Net advances artistic style transfer by enhancing attention mechanisms with a novel pattern repeatability metric, enabling better capture of style rhythm and reducing artifacts for more harmonious results.
Contribution
The paper introduces AesPA-Net, which incorporates a new pattern repeatability metric and a self-supervisory task to improve style transfer quality by capturing style rhythm and semantic correspondence.
Findings
Pattern repeatability aligns with human perception.
AesPA-Net produces more harmonious style transfers.
Outperforms existing methods in qualitative and quantitative evaluations.
Abstract
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific local patches from the style image, resulting in disharmonious and evident repetitive artifacts. To overcome this limitation and accomplish impeccable artistic style transfer, we focus on enhancing the attention mechanism and capturing the rhythm of patterns that organize the style. In this paper, we introduce a novel metric, namely pattern repeatability, that quantifies the repetition of patterns in the style image. Based on the pattern repeatability, we propose Aesthetic Pattern-Aware style transfer Networks (AesPA-Net) that…
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Code & Models
Videos
AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks· youtube
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Aesthetic Perception and Analysis · Visual Attention and Saliency Detection
MethodsFocus
